In this paper, we analyse the transient and steady–state performance of the distributed incremental least mean–square (DILMS) algorithm. theoretical relations which explain how transient and steady–state performance of DILMS algorithm (in terms of mean–square error (MSE)) is affected by non–stationary environment will be achieved. As the results show, theoretical and simulations are very close together. simulation results show that step size versus the error index curves of the non–stationary environment are not linear, and have an optimum step size for minimize the error

Least mean square (LMS) adaptive filters have been used in a wide range of signal processing application because of its simplicity in computation and implementation. However, the slow convergence of the LMS algorithm is well known. On the other hand,...

In many application of noise cancellation the changes in signal characteristics could be quite fast. This requires the utilization of adaptive algorithms , which converge rapidly . Least mean square (LMS) adaptive filters have been used in a wide...

In many applications of noise cancellation the changes in signal characteristics could be quite fast. This requires the utilization of adaptive algorithms, which converge rapidly. Least mean square (LMS) and Normalized LMS (NLMS) adaptive filters...

An FPGA–based channel noise canceller using a fixed–point standard–LMS algorithm for image transmission is proposed. The proposed core is designed in VHDL93 language as basis of FIR adaptive filter. The proposed model uses 12–bits word–length for...

In this paper, we propose a novel method, called normalized fractional least–mean–squares (NFLMS) for dual–channel speech enhancement. Here, we use a modified least–mean–squares algorithm known as fractional LMS, by incorporating the fractional term...